IEEE Symposium on

Adaptive Dynamic Programming and Reinforcement Learning

Adaptive dynamic
programming (ADP) and reinforcement learning (RL) are
two related paradigms for solving decision making problems where a
performance index must be optimized over time. ADP and RL methods are
enjoying a growing popularity and success in applications, fueled by
their ability to deal with general and complex problems, including
features such as uncertainty, stochastic effects, and nonlinearity.

The manuscripts should be submitted in PDF format. Click Here to know further guidelines for submission.

ADP
tackles these challenges by developing optimal
control methods that adapt to uncertain systems over time. A
user-defined cost function is optimized with respect to an adaptive
control law, conditioned on prior knowledge of the system and its
state, in the presence of uncertainties. A numerical search over the
present
value of the control minimizes a nonlinear cost function
forward-in-time providing a basis for real-time, approximate optimal
control. The
ability to improve performance over time subject to new or unexplored
objectives or dynamics has made ADP successful in applications from
optimal control and estimation, operation research, and computational
intelligence.

RL
takes the perspective of an agent that optimizes its behavior by
interacting with its environment and learning from the
feedback received. The long-term performance is optimized by learning a
value function that predicts the future intake of rewards over time. A
core feature of RL is that it does not require any a priori knowledge
about the environment. Therefore, the agent must explore parts of the
environment it does not know well, while at the same time exploiting
its knowledge to maximize performance. RL thus provides a framework for
learning to behave optimally in unknown environments, which has already
been applied to robotics, game playing, network management and traffic
control.

The goal of the IEEE
Symposium on ADPRL is to provide
an outlet and a forum for interaction between researchers and
practitioners in ADP and RL, in which the clear parallels between the
two fields are brought together and exploited. We equally welcome
contributions from control theory, computer science, operations
research, computational intelligence, neuroscience, as well as other
novel perspectives on ADPRL. We host original papers on methods,
analysis, applications, and overviews of ADPRL. We are interested in
applications from engineering, artificial intelligence, economics,
medicine, and other relevant fields.